Availability of data in a program determines computation stages. Incremental partial evaluation exploit these stages for optimization: it allows further specialization to be performed as data become available at later stages. The fundamental advantage of incremental specialization is to factorize the specialization process. As a result, specializing a program at a given stage costs considerably less than specializing it once all the data are available.We present a realistic and flexible approach to achieve efficient incremental run-time specialization. Rather than developing specific techniques, as previously proposed, we are able to re-use existing technology by iterating a specialization process. Moreover, in doing so, we do not lose any specialization opportunities. This approach makes it possible to exploit nested quasi-invariants and to speed up the run-time specialization process.This approach has been implemented in Tempo, a specializer for C programs that is publicly available. A preliminary experiment confirm that incremental that incremental specialization can greatly speed up the specialization process.
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